Canopy Cover As the Key Factor for Occurrence and Species Richness Of

Aquatic Botany 137 (2017) 24–29

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Aquatic Botany

jou rnal homepage: www.elsevier.com/locate/aquabot

Canopy cover as the key factor for occurrence and species richness of

subtropical stream green algae (Chlorophyta)

a,∗ b a

Cleto Kaveski Peres , Aurélio Fajar Tonetto , Michel Varajão Garey ,

Ciro Cesar Zanini Branco

Federal University of Latin American Integration (UNILA), Latin American Institute of Life and Nature Sciences, Foz do Iguac¸ u, Paraná, Brazil

São Paulo State University (UNESP), Laboratory of Aquatic Biology, Assis, São Paulo, Brazil

a r t i c l e i n f o a b s t r a c t

Article history: Understanding how local and regional environmental factors inﬂuence species richness remains a key

Received 1 March 2016

issue in ecology. Green algae (Chlorophyta) are a diverse and widely occurring group, which may be a

Received in revised form 1 November 2016

good model for studying the factors that inﬂuence species richness at local and regional scales. Here, we

Accepted 8 November 2016

tested the inﬂuence of local (water quality and structural complexity) and regional environmental factors

Available online 9 November 2016

(phyto-ecological regions) on the occurrence and species richness of macroscopic green algae (MGA)

in subtropical streams. We sampled algae in 105 streams located in the four major phyto-ecological

Keywords:

regions of the Brazilian subtropics. We used cross-transect technique in streams to sample algae and

Benthic macroalgae

environmental variables. To determine the most important variables in species occurrence and richness,

Brazilian streams

Shading we used Hierarchical Partitioning analysis (HP). We found that canopy cover alone explained 34% of MGA

Phyto-ecological region occurrence, regardless of phyto-ecological region. At the sites where MGA occurred, the species richness

was determined by both regional and local factors. The species richness was mainly inﬂuenced by phyto-

ecological region, which explained 34% of the variation in species richness, along with canopy cover and

pH which explaining 22% and 15% respectively. The highest richness was found in non-forested regions,

in transects without canopy cover, and slightly acidic pH. Our results illustrate how the combination

of regional and local factors can shape the spatial distribution of species richness and are pivotal to

understanding richness patterns at broad spatial scales.

1. Introduction Environmental gradients have been widely related to photo-

synthetic organisms in lotic habitats. For instance, nutrient (e.g.,

Hypotheses to explain species distribution in space usually conductivity, nitrogen, phosphorus), light availability (e.g., canopy

involve local and regional scales (Shurin et al., 2000). The regional cover, turbidity), temperature, pH, hydraulic conditions (e.g., water

species pool is determined by biogeographic and evolutionary velocity and depth), grazing rate, and stable substrate availability

processes, such as speciation, extinction and migration (Ricklefs, (Allan and Castillo, 2007) are the most important factors deter-

2004; Harrison and Cornell, 2008). Furthermore, these processes mining photosynthetic organisms distribution in stream realms.

are inﬂuenced by geomorphological and environmental character- These factors exhibit a wide spatial variation, so their inﬂuence is

istics (Rahbek and Graves, 2001) and historical variations in climate related to spatial scale, ranging from microhabitat to regional scale

of each region (Carnaval et al., 2009). Thus, species richness may (Frissell et al., 1986), what reﬂects in ecological patterns still not

vary between regions as a result of these factors. At the local scale, completely understood or fairly predictable. Hence, more effort is

species composition is a result of species sorting from the regional needed to uncover some neglected points regarding ecological dis-

species pool (Ricklefs, 2004; Vellend, 2010) inﬂuenced mainly by tribution of stream dwellers. For example, determining how local

local environmental characteristics (Tuomisto et al., 2014) and and regional environmental factors inﬂuence species richness is a

biotic factors (Morin, 1999), with species tracking environmental key issue in ecology (Ricklefs, 2004, 2008; Brooker et al., 2009), but

variations. this approach remains seldom applied for stream algae.

Green algae (division Chlorophyta) are important primary pro-

ducers in streams (Stevenson, 1996), commonly having higher

∗ richness than other macroalgal groups. Thus, macroscopic green

Corresponding author.

algae (MGA) are an interesting group of organisms to investigate

E-mail addresses: [email protected], [email protected] (C.K. Peres).

http://dx.doi.org/10.1016/j.aquabot.2016.11.004

C.K. Peres et al. / Aquatic Botany 137 (2017) 24–29 25

factors related to richness variations at both local and regional

scales. For example, a recent study (Branco et al., 2014) showed

that the inﬂuence of spatial (e.g. related to dispersal process) and

environmental processes (e.g. niche-based process) on the species

composition of stream macroalgae varies among taxonomic groups

(Cyanobacteria, Chlorophyta, and Rhodophyta). For instance, the

taxonomic composition of green algae is inﬂuenced by both envi-

ronment and space, while cyanobacteria only by environment and

Rhodophyta only by space (Branco et al., 2014). Thus, local and

regional factors may inﬂuence algal groups differently. Speciﬁcally

for MGA, most studies have been conducted at small spatial scales

or with limited number of species (Biggs and Price, 1987; Dodds

and Gudder, 1992; Okada and Watanabe, 2002). Most of the eco-

logical information about MGA comes from studies dealing with

all groups of stream macroalgae. In these studies, some patterns

are usually common, such as low endemism rate in each region

(Borges and Necchi, 2006), occurrence in a few streams in a region

Fig. 1. Percentage of stream transects sampled in each canopy cover class (total bars)

(Branco et al., 2009), dominance by a few species (Necchi et al.,

and percentage of streams with green algae species occurrence in each canopy cover

2000; Borges and Necchi, 2006), and local distribution in a mosaic

class (black bars).

(Necchi et al., 2000, 2003). Nevertheless, how environmental vari-

ables determine these distribution patterns is still poorly known

(Branco et al., 2009).

2. Material and methods

The major capability of green algae to grow in habitats with

higher irradiance is well accepted and recognized among special-

2.1. Study region

ists (Hill, 1996; and references therein). Indeed, previous studies

showed that unshaded environments support higher primary pro-

The sampled area comprises the Brazilian subtropical region

duction and abundance of green algae. However, the role of ◦ ◦ ◦ ◦

(33 44 59” S; 48 01 08” W and 22 31 10” S; 57 36 05” W; Branco

irradiance as a key factor on MGA occurrence and species richness is

et al., 2014; see their Fig. 1), and is included in the states of Paraná,

not clear at either regional or local scale. For example, open stream

Santa Catarina and Rio Grande do Sul, located in southern Brazil.

segments do not ensure the presence or high species richness of

The region belongs to humid subtropical zone of oceanic climate,

MGA (Branco and Necchi, 1996; Necchi et al., 2000) and the land-

without a deﬁned dry season (Alvares et al., 2013). The Brazilian

scape could be exhibiting an important contribution (Oliveira et al.,

subtropical area is dominated basically by four phyto-ecological

2013). Hence, we expect that equally opened stream segments, but

regions (IBGE, 1992) arranged in a mosaic: Seasonal Forest (SF);

from distinct phyto-ecological regions, can exhibit different species

Mixed Ombrophilous Forest (MOF); Dense Ombrophilous Forest

richness. This logic led us to assess the species richness and occur-

(DOF), and Steppe (ST). These formations were formed and shaped

rence of MGA regarding environmental factors at local and regional

by ﬁre during the quaternary climate changes (Behling and Pillar,

scales in order to check how environmental variables are determin-

2007). Climatic conditions determine these four phyto-ecological

ing the distribution pattern of MGA.

regions (IBGE, 1992). DOF has stable climate deﬁned by high and

Herein, we tested the inﬂuence of local (water quality

regular rainfall throughout the year, while in SF, MOF and ST the

and stream structural complexity) and regional factors (phyto-

precipitation is sparsely distributed along the year. Regarding tem-

ecological regions) on the occurrence and species richness of MGA

perature, ST exhibit the highest annual amplitude (winter with

in subtropical streams. We ﬁrst assessed the occurrence (presence

frost) and DOF exhibit the lowest temperature variation, while

in stream segment) of MGA among all studied streams, regardless

SF and MOF exhibit an intermediate variation. MOF cover higher

of species identity, abundance or species number. After that, we

altitude areas (more than 700 m a.s.l.) than SF.

analyzed which factors are determining the MGA species richness

We sampled 105 streams in 10 protected areas distributed in

in the streams. For this, we sampled MGA in four phyto-ecological

four vegetation formations of southern Brazil (Table 1). The region

regions that encompass very different climatic conditions and veg-

and stream segments are the same used by Branco et al. (2014) see

etation structure (mainly related to light availability). Thus, we

their Fig. 1). The sampled streams were all inside protected areas

predict that MGA species richness will be higher in phyto-ecological

to avoid the effect of anthropogenic disturbances.

regions dominated by herbaceous vegetation (steppe) due to higher

light incidence in the whole stream determined by a lower canopy

cover. This condition could possibly increase the number of species 2.2. Sampling

in the regional pool because MGA have higher photosynthetic

capability in places with high light availability (Wetzel, 2001). Fur- We sampled 105 stream segments (selected by convenience)

thermore, at local scale (e.g., within stream), other factors could in the dry periods between 2007 and 2008. The segments were

regulate the MGA species richness and, hence, we predict that such sampled once. To sample the algae, we used 10-m transects,

factors (e.g., water quality, water ﬂow, stable substrate, and canopy which were further divided into 10 1-m cross-sections (Sheath and

cover) could be inﬂuencing the species occurrence and richness of Burkholder, 1985; Necchi et al., 1995, 2000). In each stream tran-

MGA in streams. As a ﬁnal point, these factors are really impor- sect we analyzed all streambed looking for macroscopic green algae

tant for algal distribution (Stevenson, 1996), and to the best of (presence/absence of species). In order to standardize the sampling

our knowledge, this is the ﬁrst study evaluating the relationship procedure all transects were sampled for at least 30 min by the

of regional and local factors in determining geographical patterns same collectors (CKP, CCZB and AFT). When benthic green algae

in MGA species richness. occurred they were scraped off the substrate in loco using small

knives or spatulas. After we have checked all specimens present

in the stream segment, we transferred manually small parts of

them into plastic ﬂasks containing 4% formaldehyde. Thereafter,

26 C.K. Peres et al. / Aquatic Botany 137 (2017) 24–29

Table 1

Protected Areas along phyto-ecological regions (SF = Seasonal Forest; MOF = Mixed Ombrophilous Forest; DOF = Dense Ombrophilous Forest and; ST = Steppe).

Phyto-ecological region Protected area Sampled streams Streams with MGA Open canopy streams Location in map

Irati National Forest, Paraná State 11 1 0 A

MOF Araucárias State Park, Santa Catarina State 11 5 0 E

Caracol State Park, Rio Grande do Sul State 10 6 0 I

Saint-Hilaire/Lange National Park, Paraná State 14 2 0 D

DOF

Serra do Itajaí National Park, Santa Catarina State 10 3 1 F

Iguac¸ u National Park, Paraná State 10 3 0 C

SF Fritz Plaumann State Park, Santa Catarina State 9 4 1 H

Turvo State Park, Rio Grande do Sul State 10 8 2 G

Vila Velha State Park, Paraná State 10 8 6 B

Aparados da Serra National Park, Rio Grande do Sul State 10 8 9 J

In accordance with Fig. 1 of Branco et al. (2014).

we took these samples to laboratory for taxonomic identiﬁcation only for the ﬁrst distance class (Fig. S1). However, we found no spa-

under microscopy (Leica DM 1000) by using specialized literature. tial autocorrelation in the residuals of multiple regression analysis

We classiﬁed each stream according to a phyto-ecological region (Fig. S2). This suggest a low Type I error (Peres et al., 2010). Analyses

(following IBGE, 1992). Environmental variables were measured were performed with a SAM software (Rangel et al., 2010).

on each transect (Table 2). Turbidity, conductivity, and pH, were Independent quantitative variables were standardized to zero

measured at the midpoint of the stream segment using a water mean unit standard deviation prior to analysis. Multicollinearity

analyser with water quality checker Horiba U-10 (a portable elec- was assessed using the Variation Inﬂation Factor (VIF) in the R (R

tronic device with probes). The percentage of stable substrate was Core Team, 2012) package car (Fox and Weisberg, 2011). VIF > 3

visually estimated (following Gordon et al., 1992). Stable substrates indicates high multicollinearity, but the VIF of all variables was

were those larger than 25 mm (e.g., pebbles, boulder, and solid below that value.

rock). Canopy cover was estimated in the ﬁeld into four classes, We used all sampling points (n = 105) to test the inﬂuence of

following DeNicola et al. (1992): open (A), partially shaded (B), local environmental variables and regional factors on the occur-

shaded (C), and heavily shaded (D). Average water ﬂow and depth rence (presence or absence in stream segments) of MGA. However,

of the transect were obtained by averaging the values measured in we only included sampling points with at least one species (n = 48)

each section. Water flow was measured using mechanical flowme- to test the influence of environmental variables (local and regional)

ter (General Oceanics 2030R) positioned just below the surface on the species richness. To test the inﬂuence of environmental

for 20 s. Water depth was measured with a vertically positioned variables (independent variables) on the occurrence and richness

ruler. Total dissolved inorganic nitrogen and orthophosphate were (dependent variables) we used a Hierarchical Partitioning analysis

measured from a frozen water sample using a spectrophotometer (HP; Chevan and Sutherland, 1991). The HP separates independent

(Spectroquant Nova 60). variables of a multiple regression model (local or regional) that bet-

ter explain species occurrence and the distribution of MGA richness.

The HP determines the independent effects of predictor variables

2.3. Data analysis

on the dependent variable (Chevan and Sutherland, 1991). Thus, the

independent explanatory power (“I”) of each environmental vari-

We tested for spatial autocorrelation in macroalgal richness

able on the dependent variable (occurrence and species richness)

using Moran’s I correlograms with ten distance classes (Legendre

represents the independent contribution of an environmental vari-

and Legendre, 2012). Moran’s I measures the spatial autocor-

able to the variance explained by the model (Mac Nally, 2002). A

relation calculated as the product of deviations from the mean

second parameter “J” measures the interaction between each envi-

(Legendre and Legendre, 2012). Spatial autocorrelation in a given

ronmental variable and the others (Mac Nally, 2002). The results

variable can affect the results of linear models, increasing the prob-

of HP are expressed as Z-score for each independent variable. The

ability type 1 error (Diniz-Filho et al., 2003). A high positive spatial

Z-score is obtained by conducting 1000 randomizations to gener-

autocorrelation in the ﬁrst distance classes for richness means that

ate 95% conﬁdence intervals. Values of Z greater than 1.65 were

closer areas have similar richness (Diniz-Filho et al., 2003). Like-

considered signiﬁcant (Mac Nally, 2002).

wise, a high positive autocorrelation in the ﬁrst distance classes for

abundance may suggest that biotic or abiotic variables that deter-

mine abundance are spatially structured and should be considered

3. Results

in the analysis, or point to dispersal limitation (Peres et al., 2010).

We found a weak positive autocorrelation in macroalgae richness

We identiﬁed 32 species of macroscopic green algae from 11

families (Table S1). The most speciose family was Zygnemat-

Table 2

aceae with 10 species, followed by Chaetophoraceae with six, and

Independent quantitative variables used in the study with range, mean and standard

Microsporaceae and Oedogoniaceae with three species each. Only

deviation (SD) (N = 105).

Basicladia emedii Peres & Branco is an endemic species, restricted

Variables Range Mean + SD

to Seasonal Forest.

Speciﬁc conductance (Cond) 1–63 ␮S/cm 27 + 15 Approximately half (n = 48) of the sampled stream segments had

pH (pH) 4.6–7.3 6.2 + 0.7 MGA. Species occurrence was higher in transects with higher light

Turbidity (Turb) 0–86 NTU 10 + 13

intensity. Canopy cover alone explained 34% of the total variance

Average current velocity (Veloc) 5–233 cm/s 53 + 39

(Table 3; Fig. 1). Neither other local environmental variable nor

Average depth (Depth) 3–42 cm 16 + 9

vegetation formations explained signiﬁcantly species occurrence.

Orthophosphate (PO4) 0.01–0.46 mg/L 0.09 + 0.06

Total Nitrogen (N) 0.1–4.6 mg/L 0.7 + 0.8 Conversely, both local and regional variables inﬂuenced species

Stable substrate (Ssub) 0–100% 68 + 30

richness. Species richness (mean = 2.2, standard deviation + 1.6,

Note: Canopy cover (Canopy) and Phyto-ecological region (Phyto) were registred as range = 1-8, only in stream segments with MGA) was mainly inﬂu-

categoric variables.

enced by phyto-ecological region, which explained 34% of the

C.K. Peres et al. / Aquatic Botany 137 (2017) 24–29 27

Table 3

Results of Hierarchical Partitioning analysis (HP) showing only the signiﬁcant for the occurrence and species richness (as number of taxa) of green algae. Signiﬁcance values

are showed (Z-score), along with the effect of the variable alone I, interaction of this variable with other independent variables J, and totals (occurrence N = 105, species

richness N = 48).

Dependent variables Signiﬁcant independent variables Z-score Hierarchical Partitioning

I J Total

Species occurrence Canopy cover 11.47 0.1751 0.1694 0.3445

Species Phyto-ecological region 2.55 0.1851 0.1583 0.3434

rich- Canopy cover 3.08 0.0794 0.1396 0.2190

ness pH 2.82 0.0491 0.0966 0.1457

variation in species richness, along with canopy cover explaining

22%, and pH which predicted 15% of the variation (Table 3). The

most speciose phyto-ecological region was Steppe (Fig. 2A), while

the other three (SF, MOF and DOF) exhibited lower and similar val-

ues among them. The highest number of exclusive species (i.e.,

those species found exclusively in one phyto-ecological region)

were found in the steppe (Fig. S3). The relative number of MGA

species (i.e., total species number per sampling point, including all

studied streams, even those without MGA) was low in rain forests

(0.21 species per point in DOF and 0.38 in MOF), intermediate

in seasonal forest (0.52), and high in steppe (0.80). The greatest

species richness was found in stream segments with high light

incidence (Fig. 2B). Another key variable was pH and the highest

richness (more than three species per sampling segment) occurred

in stream segments with pH ranging from 5.2 to 6.5 (Fig. 3).

4. Discussion

Fig. 3. Species richness of macroscopic green algae in relation to stream pH. Filled

Our results showed that both local and regional factors inﬂuence circles represent streams without shading from canopy in Steppe regions, and with

pH between 5.2 and 5.8 (N = 48).

the distribution of green algae in subtropical streams. However,

light incidence stood out as the most important local variable inﬂu-

encing both species occurrence and richness. These results concur capture other portions of the PAR spectrum (blue and red), enhanc-

with previous studies showing that green algae have a higher pho- ing the light absorption (Wetzel, 2001; Necchi, 2004). Thus, the

tosynthetic efﬁciency in places with lower canopy cover (Hill, 1996; transfer light energy mediated by carotenoids and chlorophyll b to

Necchi, 2004). Thus, the green algae occurrence increases due to the chlorophyll a could explain the higher presence of MGA in open-

high energy input (Okada and Watanabe, 2002). canopy streams.

Green algae physiological features can explain partly the Canopy cover has been related not only to the seasonal varia-

preference to stream segments with lower canopy cover and con- tion in richness of green algae (Sheath and Burkholder, 1985), but

sequently more light incidence. This algal group contain a peculiar also its local abundance (e.g., Sheath et al., 1996; Tonetto et al.,

pigment represented mainly by chlorophyll a, b and carotenoids 2012) and species distribution (e.g., Everitt and Burkholder, 1991;

(Lee, 2008). For instance, carotenoids protect the photosynthetic Peres et al., 2009). Other studies regarding tropical and subtropi-

apparatus against high light intensities and, hence, photoinhibi- cal stream macroalgae assessment have not directly addressed the

tion is low or absent in green algae from well-lit streams (Hill, relationship between stream green algae and light incidence (e.g.,

1996; Necchi, 2004). Furthermore, differently from any other algal Necchi et al., 1995; Branco and Necchi, 1996; Krupek et al., 2007;

group, green algae exhibit high amounts of chlorophyll b that Branco et al., 2009). However, by looking carefully at their data it is

Fig. 2. Box plot (median and interquartile range) of species richness values of macroscopic green algae (MGA) per stream sampled A) in each phyto-ecological region.

(SF = Seasonal Forest; MOF = Mixed Ombrophilous Forest; DOF = Dense Forest and Ombrophilous; ST = Steppe). B) in each canopy cover class. Only stream segments with MGA

occurrence were considered (N = 48).

28 C.K. Peres et al. / Aquatic Botany 137 (2017) 24–29

possible to notice a trend of green algae occurrence in stream seg- determining geographical patterns in green algae species richness.

ments with high light availability. Hence, our study is the ﬁrst one Previous studies have demonstrated that the relationship between

that directly links the inﬂuence of canopy cover on the occurrence local and regional factors are an important mechanism in determin-

and richness of green algae at a regional spatial scale. ing the diversity patterns of other taxonomic groups in freshwater

Our study was carried out in both forested and non-forested habitats (Heino, 2001; Passy, 2009; Lombardo et al., 2013). Both

regions. However, most species occurred in the Steppe, despite the ecological and evolutionary processes affect the distribution of

predominance of streams in forested regions in our data set. Our biodiversity (e.g., Harrison and Cornell, 2008), a claim that is sup-

results suggests that light incidence is key not only at local scales ported by many empirical studies (e.g., Buckley and Jetz, 2007). Our

(i.e., transect), but also at regional scales, where stream segments results showed that phyto-ecological region, where the streams

in regions with herbaceous plants have higher species richness of are inserted (i.e, regional factors), explained great part of the MGA

green algae. Stream segments with higher local richness occurred in richness variation among the streams (34% of total variation. See

regions with also high richness. The inﬂuence of regional factors on Table 3). Hence, after the inﬂuence of regional factors the local envi-

green algae richness can be explained by the “species pool hypoth- ronmental factors begin to exert an essential inﬂuence on species

esis”, in which local richness patterns vary as a result of large scale richness variation of green algae in streams (mainly canopy cover

historical factors (Harrison and Cornell 2008; Zobel et al., 2011; and pH that explained 22% and 15% of the total variation, respec-

Cornell and Harrison, 2014). According to this hypothesis, variation tively. See Table 3).

in species richness is explained by changes in extinction, coloniza- In summary, we suggest that canopy cover is the main variable

tion and speciation rates of each phyto-ecological region (Taylor inﬂuencing the occurrence of MGA, regardless of phyto-ecological

et al., 1990; Schamp et al., 2002). For stream macroalgae in gen- region. Furthermore, light availability and water pH seemed to be

eral, endemism is low (Borges and Necchi, 2006) so within a region the key environmental ﬁlters inﬂuencing species richness. Thus,

the main richness increase occurs by colonization of species from the greatest species richness were found in non-forest formations,

other regions. Thus, the species pool of phyto-ecological region will in transects without shading from canopy, and slightly acidic pH.

inﬂuence the species richness in each stream segment. Taken together, our results show how local and regional factors can

Our work showed that phyto-ecological regions with more sta- be integrated to understanding species richness patterns at large

ble climate (with minimal seasonal variation in weather patterns), spatial scales.

as dense ombrophilous forest, have a lower relative species richness

of MGA than regions whose seasonal changes are more evident, as Acknowledgments

steppe. Studies conducted in other countries also showed similar

results. For example, a study comprising 1000 stream segments

The authors thank IMEA/PRPPG UNILA for ﬁnancial support,

in North America (Sheath and Cole, 1992) suggested that MGA

FAPESP (Proc. 2007/52608-1) for ﬁnancial support of the project

richness among biomes was relatively similar. However, when

“Macroalgas de riachos do Sul do Brasil”, CNPq for a doctoral schol-

we re-analyzed their data weighting the species richness by the

arship to CKP (Proc. 141754/2007-9) and a research grant to CCZB

number of sampling points, the highest species richness was in

(Proc. 302354/2008-5), and ICMBio and the conservation units that

Chaparral (0.46 species per transect), while the lowest was found

were mentioned for granting permission to collect specimens and

in Tropical Rain Forest (0.09 species per transect). Therefore, their

providing logistical support during our ﬁeld work. The authors are

data showed the same trend of our study. In both cases, streams

grateful for the suggestions of Lilian Sayuri Ouchi de Melo and

from forested formations in areas with stable and warm climate

anonymous referees.

have lower algae richness. Interestingly, a greater MGA diversity

has been found in open vegetations, like Chaparral and Steppe, than

Appendix A. Supplementary data

tropical or temperate forests, even though the former are harsh

environment for many plants and animals.

Supplementary data associated with this article can be found, in

The pH along with canopy cover was a key local variable in

the online version, at http://dx.doi.org/10.1016/j.aquabot.2016.11.

determining MGA species richness patterns. There was a narrow

004.

variation in pH in the sampled stream segments, from neutral to

slightly acidic, since we only sampled natural areas. However, the

effect of this variable on the species richness was hump-shaped, References

with the greatest species richness in pH ranging between 5.2

Allan, J.D., Castillo, M.M., 2007. Stream Ecology: Structure and Function of Running

and 6.5. The availability of inorganic carbon (essential for pho-

Waters, 2nd ed. Springer, Netherlands.

tosynthesis) in aquatic environments is usually regulated by pH

Alvares, C.A., Stape, J.L., Sentelhas, P.C., Gonc¸ alves, J.L.M., Sparovek, G., 2013.

(Allan and Castillo, 2007). Thus, different pH ranges determine the Köppen’s climate classiﬁcation map for Brazil. Meteorol. Z. 22 (6), 711–728.

− −

2 Behling, H., Pillar, V.D., 2007. Late Quaternary vegetation, biodiversity and ﬁre

prevalence of various forms of carbon (CO2, HCO3 , and CO3 )

dynamics on the southern Brazilian highland and their implication for

that can be favorable to different photosynthetic organism groups

conservation and management of modern Araucaria forest and grassland

(Maberly and Spence, 1983). The study by Sheath et al. (1989) ecosystems. Philos. Trans. R. Soc. B 362, 243–251.

Biggs, B.J.F., Price, G.M.A., 1987. A survey of ﬁlamentous algal proliferation in New

found a negative correlation between Chlorophyta diversity and

Zealand rivers. New Zeal. J. Mar. Fresh. Res. 21, 175–191.

pH, arguing that many species of green algae are not able to use

− Borges, F.R., Necchi Jr., O., 2006. Patterns of spatial distribution in macroalgal

HCO3 as inorganic carbon source. The pH range (tending to slightly communities from tropical lotic ecosystems. Rev. Bras. Bot. 29 (4), 669–680.

Branco, C.C.Z., Necchi Jr., O., 1996. Distribution of stream macroalgae in the eastern

acid) where we found the greatest species richness corresponds to

Atlantic Rainforest of São Paulo State, southeastern Brazil. Algol. Stud. 333,

high CO availability (Allan and Castillo, 2007), being probably the

2 139–150.

optimum for many MGA species. Filamentous green algae (espe- Branco, C.C.Z., Krupek, R.A., Peres, C.K., 2009. Ecological distribution of stream

cially Zygnemataceae) can dominate aquatic ecosystems affected macroalgal communities from mid-western region of Paraná State, Southern

Brazil: importance of local scale variation. Braz. Arch. Biol Technol. 52 (2),

by acidiﬁcation (Planas, 1996). Thus, the hump-shaped relation-

379–386.

ship between richness and pH reinforces the importance of local

Branco, C.C.Z., Bispo, P.C., Peres, C.K., Tonetto, A.F., Branco, L.H.Z., 2014. The roles of

environmental factors on algal distribution. Then, most of the MGA environmental conditions and spatial factors in controlling stream macroalgal

communities. Hydrobiologia 732, 123–132.

in the study area seems to be adapted to the slightly acidic pH.

Brooker, R.W., Callaway, R.M., Cavieres, L.A., Kikvidze, Z., Lortie, C.J., Michalet, R.,

Our study showed the importance of both regional (i.e., phyto-

Pugnaire, F.I., Valiente-Banuet, A., Whitham, T.G., 2009. Don’t dissintegration: a

ecological region) and local factors (i.e., canopy cover and pH) in comment on Ricklefs’ disintegrating communities. Am. Nat. 174, 919–927.

C.K. Peres et al. / Aquatic Botany 137 (2017) 24–29 29

Buckley, L.B., Jetz, W., 2007. Environmental and historical constraints on global Oliveira, R.C., Tonetto, A.F., Peres, C.K., Branco, C.C.Z., 2013. The landscape inﬂuence

patterns of amphibian richness. Proc. R. Soc. Lond. B Biol. 274, 1167–1173. on the spatial and temporal distribution of stream macroalgal communities of

Carnaval, A.C., Hickerson, M.J., Haddad, C.F.B., Rodrigues, M.T., Moritz, C., 2009. two types of subtropical biomes. Limnética 32, 287–302.

Stability predicts genetic diversity in the Brazilian atlantic forest hotspot. Passy, S.I., 2009. The relationship between local and regional diatom richness is

Science 323, 785–789. mediated by the local and regional environment. Glob. Ecol. Biogeogr. 8,

Chevan, A., Sutherland, M., 1991. Hierarchical partitioning. Am. Stat. 45, 90–96. 383–391.

Cornell, H.V., Harrison, S.P., 2014. What are species pools and when are they Peres, C.K., Branco, C.C.Z., Krupek, R.A., 2009. Distribuic¸ ão ambiental e temporal

important? Annu. Rev. Ecol. Evol. Syst. 45, 45–67. das comunidades de macroalgas da Serra da Prata, estado do Paraná, Sul do

DeNicola, D.M., Hogland, K.D., Roemer, S.C., 1992. Inﬂuence of canopy cover on Brasil. Rev. Bras. Bot. 32 (4), 625–633.

spectral irradiance and periphyton assemblages in a prairie stream. J. N. Am. Peres-Neto, P.R., Legendre, P., 2010. Estimating and controlling for spatial structure

Benthol. Soc. 11, 391–404. in the study of ecological communities. Global Ecol. Biogeogr. 19, 174–184.

Diniz-Filho, J.A.F., Bini, L.M., Hawkins, B.A., 2003. Spatial autocorrelation and red Planas, D., 1996. Acidiﬁcation effects. In: Stevenson, R.J., Bothwell, M.L., Lowe, R.L.

herrings in geographical ecology. Global Ecol. Biogeogr. 12, 53–64. (Eds.), Algal Ecology: Freshwater Benthic Ecosystem. Academic Press, San

Dodds, W.K., Gudder, D.A., 1992. The ecology of Cladophora. J. Phycol. 28, 415–427. Diego, CA, pp. 497–530.

Everitt, D.T., Burkholder, J.M., 1991. Seasonal dynamics of macrophyte R Core Team, 2012. R: a Language and Environment for Statistical Computing. R

communities from a stream ﬂowing over granite ﬂatrock in North Carolina, Foundation for Statistical Computing, Vienna, Austria (URL http://www.R-

USA. Hydrobiologia 222 (3), 159–172. project.org/).

Fox, J., Weisberg, S., 2011. An R Companion to Applied Regression, 2nd ed. Rahbek, C., Graves, G.R., 2001. Multiscale assessment of patterns of avian species

Thousand Oaks, Sage. richness. Proc. Natl. Acad. Sci. Biol. 98, 4534–4539.

Frissell, C.A., Liss, W.J., Warren, C.E., Hurley, M.D., 1986. A hierarchical framework Rangel, T.F., Diniz-Filho, J.A.F., Bini, L.M., 2010. SAM: a comprehensive application

for stream habitat classiﬁcation viewing streams in a watershed context. for spatial analysis in macroecology. Ecography 33, 46–50.

Environ. Manage. 10 (2), 199–214. Ricklefs, R.E., 2004. A comprehensive framework for global patterns in

Gordon, N.D., McMahon, T.A., Finlayson, B.L., 1992. Stream Hydrology: an biodiversity. Ecol. Lett. 7, 1–15.

Introduction for Ecologists. John Wiley & Sons, Chichester. Ricklefs, R.E., 2008. Disintegration of the ecological community. Am. Nat. 172,

Harrison, S., Cornell, H., 2008. Toward a better understanding of the regional 741–750.

causes of local community richness. Ecol. Lett. 11, 969–979. Schamp, B.S., Laird, R.A., Aarssen, L.W., 2002. Fewer species because of uncommon

Heino, J., 2001. Regional gradient analysis of freshwater biota: do similar habitat? Testing the species pool hypothesis for low plant species richness in

biogeographic patterns exist among multiple taxonomic groups? J. Biogeogr. highly productive habitats. Oikos 97, 145–152.

28, 69–76. Sheath, R.G., Burkholder, J., 1985. Characteristics of softwater stream in Rhode

Hill, W.R., 1996. Effects of light. In: Stevenson, R.J., Bothwell, M.L., Lowe, R.L. (Eds.), Island: II: composition and seasonal dynamics of macroalgae communities.

Algal Ecology: Freshwater Benthic Ecosystem. Academic Press, San Diego, CA, Hydrobiologia 128, 109–118.

pp. 121–149. Sheath, R.G., Cole, K.M., 1992. Biogeography of stream macroalgae in North

IBGE, 1992. Instituto brasileiro de geograﬁa e estatística. In: Manual Técnico Da America. J. Phycol. 28, 448–460.

Vegetac¸ ão Brasileira. Série Manuais Técnicos em Geociências, Rio de Janeiro n. Sheath, R.G., Hamilton, P.B., Hambrook, J.A., Cole, K.M., 1989. Stream macroalgae of

07. Eastern boreal forest region of North America. Can. J. Bot. 67, 3553–3562.

Krupek, R.A., Branco, C.C.Z., Peres, C.K., 2007. Distribuic¸ ão ecológica das Sheath, R.G., Vis, M.L., Hambrook, J.A., Cole, K.M., 1996. Tundra stream macroalgae

comunidades de macroalgas da bacia de drenagem do Rio das Pedras, região of North America: composition, distribution and physiological adaptations.

centro-sul do estado do Paraná, Sul do Brasil. Rev. Bras. Bot. 30 (2), 173–182. Hydrobiologia 336, 67–82.

Lee, R.E., 2008. Phycology, 4th ed. Cambridge University Press, Cambridge. Shurin, J.B., Havel, J.E., Leibold, M.A., Pinel-Alloul, B., 2000. Local and regional

Legendre, P., Legendre, L., 2012. Numerical Ecology, 3rd ed. Elsevier Science, zooplankton species richness: a scale-independent test for saturation. Ecology

Amsterdam. 81, 3062–3073.

Lombardo, P., Mjelde, M., Källqvist, T., Brettum, P., 2013. Seasonal and Stevenson, R.J., 1996. An introduction to algal ecology in freshwater benthic

scale-dependent variability in nutrient- and allelopathy-mediated habitats. In: Stevenson, R.J., Bothwell, M.L., Lowe, R.L. (Eds.), Algal Ecology:

macrophyte–phytoplankton interactions. Knowl. Manage. Aquat. Ecol. 409, 10. Freshwater Benthic Ecosystem. Academic Press, San Diego, CA, pp. 3–30.

Maberly, S.C., Spence, D.H.N., 1983. Photosynthetic inorganic carbon use by Taylor, D.R., Aarssen, L.W., Loehle, C., 1990. On the relationship between r/K

freshwater plants. J. Ecol. 71, 705–724. selection and environmental carrying capacity: a new habitat templet for plant

Mac Nally, R., 2002. Multiple regression and inference in ecology and conservation life history strategies. Oikos 58, 239–250.

biology: further comments on identifying important predictor variables. Tonetto, A.F., Branco, C.C.Z., Peres, C.K., 2012. The effects of irradiance and spectral

Biodivers. Conserv. 11, 1397–1401. composition on the establishment of macroalgae in streams in southern Brazil.

Morin, P.J., 1999. Community Ecology. Blackwell Malden, Massachusetts. Ann. Limnol.-Int. J. Lim. 48 (4), 363–370.

Necchi Jr., O., Branco, C.C.Z., Simões, R.C.G., Branco, L.H.Z., 1995. Distribution of Tuomisto, H., Zuquim, G., Cárdenas, G., 2014. Species richness and diversity along

stream macroalgae in northwest region of Sõo Paulo State, Southeastern Brazil. edaphic and climatic gradients in Amazonia. Ecography 37, 1034–1046.

Hydrobiologia 299, 219–230. Vellend, M., 2010. Conceptual synthesis in community ecology. Q. Rev. Biol. 85,

Necchi Jr., O., Branco, C.C.Z., Branco, L.H.Z., 2000. Distribution of stream macroalgae 183–206.

in São paulo state, southeastern Brazil. Algol. Stud. 97, 43–57. Wetzel, R.G., 2001. Limnology: Lake and River Ecosystems, 3rd ed. Academic Press,

Necchi Jr., O., Branco, L.H.Z., Branco, C.C.Z., 2003. Ecological distribution of stream San Diego, CA.

macroalgal communities from a drainage basin in the Serra da Canastra Zobel, M., Otto, R., Laanisto, L., Naranjo-Cigala, A., Pärtel, M., Fernández-Palacios,

National Park, Minas Gerais, Southeastern Brazil. Braz. J. Biol. 63, 1–12. J.M., 2011. The formation of species pools: historical habitat abundance affects

Necchi Jr., O., 2004. Light-related photosynthetic characteristics of lotic current local diversity. Global Ecol. Biogeogr. 20, 251–259.

macroalgae. Hydrobiologia 525, 139–155.

Okada, H., Watanabe, Y., 2002. Effect of physical factors on the distribution of

ﬁlamentous green algae in the Tama River. Limnology 3, 121–126.